OpenRaiser/Intern-Atlas huggingface dataset:
Dual-Lagrange Encoding for Storage and Download in Elastic Computing for Resilience
Venue: arXiv cs.IT 2025
Year: 2025

Coded elastic computing enables virtual machines to be preempted for high-priority tasks while allowing new virtual machines to join ongoing computation seamlessly. This paper addresses coded elastic computing for matrix-matrix multiplications with straggler tolerance by encoding both storage and download using Lagrange codes. In 2018, Yang et al. introduced the first coded elastic computing scheme for matrix-matrix multiplications, achieving a lower computational load requirement. However, this scheme lacks straggler tolerance and suffers from high upload cost. Zhong et al. (2023) later tackled these shortcomings by employing uncoded storage and Lagrange-coded download. However, their approach requires each machine to store the entire dataset. This paper introduces a new class of elastic computing schemes that utilize Lagrange codes to encode both storage and download, achieving a reduced storage size. The proposed schemes efficiently mitigate both elasticity and straggler effects, with a storage size reduced to a fraction $\frac{1}{L}$ of Zhong et al.'s approach, at the expense of doubling the download cost. Moreover, we evaluate the proposed schemes on AWS EC2 by measuring computation time under two different tasks allocations: heterogeneous and cyclic assignments. Both assignments minimize computation redundancy of the system while distributing varying computation loads across machines.
Programming in Brazilian Higher Education and High School: A Systematic Literature Review
Venue: arXiv cs.CY 2025
Year: 2025

Programming, which is both economically significant and mentally stimulating, has been found to benefit the aging brain and to enhance cognitive function at various educational levels. Despite its advantages, challenges persist in standardizing and implementing programming education effectively across both the higher and secondary education levels in Brazil. To shed light on these issues, we carried out a systematic review of programming teaching methods in the Brazilian context, examining gaps, common techniques, approaches, and action opportunities in programming education. Our findings provide valuable recommendations for educational policymakers and educators to develop effective and updated national policies to teach programming.
Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange
Venue: arXiv cs.LG 2025
Year: 2025

Gradient-based Discrete Samplers (GDSs) are effective for sampling discrete energy landscapes. However, they often stagnate in complex, non-convex settings. To improve exploration, we introduce the Discrete Replica EXchangE Langevin (DREXEL) sampler and its variant with Adjusted Metropolis (DREAM). These samplers use two GDSs at different temperatures and step sizes: one focuses on local exploitation, while the other explores broader energy landscapes. When energy differences are significant, sample swaps occur, which are determined by a mechanism tailored for discrete sampling to ensure detailed balance. Theoretically, we prove that the proposed samplers satisfy detailed balance and converge to the target distribution under mild conditions. Experiments across 2d synthetic simulations, sampling from Ising models and restricted Boltzmann machines, and training deep energy-based models further confirm their efficiency in exploring non-convex discrete energy landscapes.
CardiCat: a Variational Autoencoder for High-Cardinality Tabular Data
Venue: arXiv cs.LG 2025
Year: 2025

High-cardinality categorical features are a common characteristic of mixed-type tabular datasets. Existing generative model architectures struggle to learn the complexities of such data at scale, primarily due to the difficulty of parameterizing the categorical features. In this paper, we present a general variational autoencoder model, CardiCat, that can accurately fit imbalanced high-cardinality and heterogeneous tabular data. Our method substitutes one-hot encoding with regularized dual encoder-decoder embedding layers, which are jointly learned. This approach enables us to use embeddings that depend also on the other covariates, leading to a compact and homogenized parameterization of categorical features. Our model employs a considerably smaller trainable parameter space than competing methods, enabling learning at a large scale. CardiCat generates high-quality synthetic data that better represent high-cardinality and imbalanced features compared to competing VAE models for multiple real and simulated datasets.
A Geometric Perspective for High-Dimensional Multiplex Graphs
Venue: arXiv cs.LG 2025
Year: 2025

High-dimensional multiplex graphs are characterized by their high number of complementary and divergent dimensions. The existence of multiple hierarchical latent relations between the graph dimensions poses significant challenges to embedding methods. In particular, the geometric distortions that might occur in the representational space have been overlooked in the literature. This work studies the problem of high-dimensional multiplex graph embedding from a geometric perspective. We find that the node representations reside on highly curved manifolds, thus rendering their exploitation more challenging for downstream tasks. Moreover, our study reveals that increasing the number of graph dimensions can cause further distortions to the highly curved manifolds. To address this problem, we propose a novel multiplex graph embedding method that harnesses hierarchical dimension embedding and Hyperbolic Graph Neural Networks. The proposed approach hierarchically extracts hyperbolic node representations that reside on Riemannian manifolds while gradually learning fewer and more expressive latent dimensions of the multiplex graph. Experimental results on real-world high-dimensional multiplex graphs show that the synergy between hierarchical and hyperbolic embeddings incurs much fewer geometric distortions and brings notable improvements over state-of-the-art approaches on downstream tasks.
Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains
Venue: arXiv cs.CL 2025
Year: 2025

Question generation in education is a time-consuming and cognitively demanding task, as it requires creating questions that are both contextually relevant and pedagogically sound. Current automated question generation methods often generate questions that are out of context. In this work, we explore advanced techniques for automated question generation in educational contexts, focusing on In-Context Learning (ICL), Retrieval-Augmented Generation (RAG), and a novel Hybrid Model that merges both methods. We implement GPT-4 for ICL using few-shot examples and BART with a retrieval module for RAG. The Hybrid Model combines RAG and ICL to address these issues and improve question quality. Evaluation is conducted using automated metrics, followed by human evaluation metrics. Our results show that both the ICL approach and the Hybrid Model consistently outperform other methods, including baseline models, by generating more contextually accurate and relevant questions.
Cute-Lock: Behavioral and Structural Multi-Key Logic Locking Using Time Base Keys
Venue: arXiv cs.CR 2025
Year: 2025

The outsourcing of semiconductor manufacturing raises security risks, such as piracy and overproduction of hardware intellectual property. To overcome this challenge, logic locking has emerged to lock a given circuit using additional key bits. While single-key logic locking approaches have demonstrated serious vulnerability to a wide range of attacks, multi-key solutions, if carefully designed, can provide a reliable defense against not only oracle-guided logic attacks, but also removal and dataflow attacks. In this paper, using time base keys, we propose, implement and evaluate a family of secure multi-key logic locking algorithms called Cute-Lock that can be applied both in RTL-level behavioral and netlist-level structural representations of sequential circuits. Our extensive experimental results under a diverse range of attacks confirm that, compared to vulnerable state-of-the-art methods, employing the Cute-Lock family drives attacking attempts to a dead end without additional overhead.
WCDT: Systematic WCET Optimization for Decision Tree Implementations
Venue: arXiv cs.LG 2025
Year: 2025

Machine-learning models are increasingly deployed on resource-constrained embedded systems with strict timing constraints. In such scenarios, the worst-case execution time (WCET) of the models is required to ensure safe operation. Specifically, decision trees are a prominent class of machine-learning models and the main building blocks of tree-based ensemble models (e.g., random forests), which are commonly employed in resource-constrained embedded systems. In this paper, we develop a systematic approach for WCET optimization of decision tree implementations. To this end, we introduce a linear surrogate model that estimates the execution time of individual paths through a decision tree based on the path's length and the number of taken branches. We provide an optimization algorithm that constructively builds a WCET-optimal implementation of a given decision tree with respect to this surrogate model. We experimentally evaluate both the surrogate model and the WCET-optimization algorithm. The evaluation shows that the optimization algorithm improves analytically determined WCET by up to $17\%$ compared to an unoptimized implementation.
Throwaway Accounts and Moderation on Reddit
Venue: arXiv cs.HC 2025
Year: 2025

Social media platforms (SMPs) facilitate information sharing across varying levels of sensitivity. A crucial design decision for SMP administrators is the platform's identity policy, with some opting for real-name systems while others allow anonymous participation. Content moderation on these platforms is conducted by both humans and automated bots. This paper examines the relationship between anonymity, specifically through the use of ``throwaway'' accounts, and the extent and nature of content moderation on Reddit. Our findings indicate that content originating from anonymous throwaway accounts is more likely to violate rules on Reddit. Thus, they are more likely to be removed by moderation than standard pseudonymous accounts. However, the moderation actions applied to throwaway accounts are consistent with those applied to ordinary accounts, suggesting that the use of anonymous accounts does not necessarily necessitate increased human moderation. We conclude by discussing the implications of these findings for identity policies and content moderation strategies on SMPs.
A Tale of Three Location Trackers: AirTag, SmartTag, and Tile
Venue: arXiv cs.PF 2025
Year: 2025

Bluetooth Low Energy (BLE) location trackers, or "tags", are popular consumer devices for monitoring personal items. These tags rely on their respective network of companion devices that are capable of detecting their BLE signals and relay location information back to the owner. While manufacturers claim that such crowd-sourced approach yields accurate location tracking, the tags' real-world performance characteristics remain insufficiently understood. To this end, this study presents a comprehensive analysis of three major players in the market: Apple's AirTag, Samsung's SmartTag, and Tile. Our methodology combines controlled experiments -- with a known large distribution of location-reporting devices -- as well as in-the-wild experiments -- with no control on the number and kind of reporting devices encountered, thus emulating real-life use-cases. Leveraging data collection techniques improved from prior research, we recruit 22 volunteers traveling across 29 countries, examining the tags' performance under various environments and conditions. Our findings highlight crucial updates in device behavior since previous studies, with AirTag showing marked improvements in location report frequency. Companion device density emerged as the primary determinant of tag performance, overshadowing technological differences between products. Additionally, we find that post-COVID-19 mobility trends could have contributed to enhanced performance for AirTag and SmartTag. Tile, despite its cross-platform compatibility, exhibited notably lower accuracy, particularly in Asia and Africa, due to limited global adoption. Statistical modeling of spatial errors -- measured as the distance between reported and actual tag locations -- shows log-normal distributions across all tags, highlighting the need for improved location estimation methods to reduce occasional significant inaccuracies.
Remote State Estimation over a Wearing Channel: Information Freshness vs. Channel Aging
Venue: arXiv cs.IT 2025
Year: 2025

We study the remote estimation of a linear Gaussian system over a nonstationary channel that wears out over time and with every use. The sensor can either transmit a fresh measurement in the current time slot, restore the channel quality at the cost of downtime, or remain silent. More frequent transmissions yield accurate estimates but incur significant wear on the channel. Renewing the channel too often improves channel conditions but results in poor estimation quality. What is the optimal timing to transmit measurements and restore the channel? We formulate the problem as a Markov decision process (MDP) and show the monotonicity properties of an optimal policy. A structured policy iteration algorithm is proposed to find the optimal policy.
Wireless Network Topology Inference: A Markov Chains Approach
Venue: arXiv cs.NI 2025
Year: 2025

We address the problem of inferring the topology of a wireless network using limited observational data. Specifically, we assume that we can detect when a node is transmitting, but no further information regarding the transmission is available. We propose a novel network estimation procedure grounded in the following abstract problem: estimating the parameters of a finite discrete-time Markov chain by observing, at each time step, which states are visited by multiple ``anonymous'' copies of the chain. We develop a consistent estimator that approximates the transition matrix of the chain in the operator norm, with the number of required samples scaling roughly linearly with the size of the state space. Applying this estimation procedure to wireless networks, our numerical experiments demonstrate that the proposed method accurately infers network topology across a wide range of parameters, consistently outperforming transfer entropy, particularly under conditions of high network congestion.
Towards Supporting Penetration Testing Education with Large Language Models: an Evaluation and Comparison
Venue: arXiv cs.CR 2025
Year: 2025

Cybersecurity education is challenging and it is helpful for educators to understand Large Language Models' (LLMs') capabilities for supporting education. This study evaluates the effectiveness of LLMs in conducting a variety of penetration testing tasks. Fifteen representative tasks were selected to cover a comprehensive range of real-world scenarios. We evaluate the performance of 6 models (GPT-4o mini, GPT-4o, Gemini 1.5 Flash, Llama 3.1 405B, Mixtral 8x7B and WhiteRabbitNeo) upon the Metasploitable v3 Ubuntu image and OWASP WebGOAT. Our findings suggest that GPT-4o mini currently offers the most consistent support making it a valuable tool for educational purposes. However, its use in conjonction with WhiteRabbitNeo should be considered, because of its innovative approach to tool and command recommendations. This study underscores the need for continued research into optimising LLMs for complex, domain-specific tasks in cybersecurity education.
Understanding Trust in Authentication Methods for Icelandic Digital Public Services
Venue: arXiv cs.CR 2025
Year: 2025

Digital public services have revolutionised citizen and private sector interactions with governments. Certain communities are strongly dependent on such digital services for ensuring the availability of public services due to geographical isolation or the presence of adverse geophysical and weather phenomena. However, strong and effective security is key to maintaining the integrity of public records and services yet also for ensuring trust in them. Trust is essential for user uptake, particularly given a global increase in data-protection concerns and a turbulent geopolitical security environment. In this paper, we examine the case of public trust in various forms of authentication for electronic identification in Iceland, which has high availability requirements for digital public services due to its unique and dynamic geophysical characteristics. Additionally, Iceland has historically low levels of institutional trust which may conflict with the requirement for an increased need for digital public services. Through surveying the Icelandic general public, we find that there is a high-level of trust in digital identification services across all demographics. We conclude with a discussion and future research challenges towards improving the effectiveness of authentication considering the diverse groups within Icelandic society, such as the rapidly increasing population of migrants and the large and dynamic population of tourists.
Action Recognition Using Temporal Shift Module and Ensemble Learning
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents the first-rank solution for the Multi-Modal Action Recognition Challenge, part of the Multi-Modal Visual Pattern Recognition Workshop at the \acl{ICPR} 2024. The competition aimed to recognize human actions using a diverse dataset of 20 action classes, collected from multi-modal sources. The proposed approach is built upon the \acl{TSM}, a technique aimed at efficiently capturing temporal dynamics in video data, incorporating multiple data input types. Our strategy included transfer learning to leverage pre-trained models, followed by meticulous fine-tuning on the challenge's specific dataset to optimize performance for the 20 action classes. We carefully selected a backbone network to balance computational efficiency and recognition accuracy and further refined the model using an ensemble technique that integrates outputs from different modalities. This ensemble approach proved crucial in boosting the overall performance. Our solution achieved a perfect top-1 accuracy on the test set, demonstrating the effectiveness of the proposed approach in recognizing human actions across 20 classes. Our code is available online https://github.com/ffyyytt/TSM-MMVPR.
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1.5 cases per 100,000. Early detection of pNETs is critical for improving patient survival, but the rarity of pNETs makes segmenting them from CT a very challenging problem. So far, there has not been a dataset specifically for pNETs available to researchers. To address this issue, we propose a pNETs dataset, a well-annotated Contrast-Enhanced Computed Tomography (CECT) dataset focused exclusively on Pancreatic Neuroendocrine Tumors, containing data from 469 patients. This is the first dataset solely dedicated to pNETs, distinguishing it from previous collections. Additionally, we provide the baseline detection networks with a new slice-wise weight loss function designed for the UNet-based model, improving the overall pNET segmentation performance. We hope that our dataset can enhance the understanding and diagnosis of pNET Tumors within the medical community, facilitate the development of more accurate diagnostic tools, and ultimately improve patient outcomes and advance the field of oncology.
Graphs of unbounded linear cliquewidth must transduce all trees
Venue: arXiv cs.LO 2025
Year: 2025

The Pathwidth Theorem states that if a class of graphs has unbounded pathwidth, then it contains all trees as graph minors. We prove a similar result for dense graphs: if a class of graphs has unbounded linear cliquewidth, then it can produce all trees via some fixed CMSO transduction.
Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding
Venue: arXiv cs.SD 2025
Year: 2025

Efficiently compressing high-dimensional audio signals into a compact and informative latent space is crucial for various tasks, including generative modeling and music information retrieval (MIR). Existing audio autoencoders, however, often struggle to achieve high compression ratios while preserving audio fidelity and facilitating efficient downstream applications. We introduce Music2Latent2, a novel audio autoencoder that addresses these limitations by leveraging consistency models and a novel approach to representation learning based on unordered latent embeddings, which we call summary embeddings. Unlike conventional methods that encode local audio features into ordered sequences, Music2Latent2 compresses audio signals into sets of summary embeddings, where each embedding can capture distinct global features of the input sample. This enables to achieve higher reconstruction quality at the same compression ratio. To handle arbitrary audio lengths, Music2Latent2 employs an autoregressive consistency model trained on two consecutive audio chunks with causal masking, ensuring coherent reconstruction across segment boundaries. Additionally, we propose a novel two-step decoding procedure that leverages the denoising capabilities of consistency models to further refine the generated audio at no additional cost. Our experiments demonstrate that Music2Latent2 outperforms existing continuous audio autoencoders regarding audio quality and performance on downstream tasks. Music2Latent2 paves the way for new possibilities in audio compression.
CSEval: Towards Automated, Multi-Dimensional, and Reference-Free Counterspeech Evaluation using Auto-Calibrated LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Counterspeech has emerged as a popular and effective strategy for combating online hate speech, sparking growing research interest in automating its generation using language models. However, the field still lacks standardised evaluation protocols and reliable automated evaluation metrics that align with human judgement. Current automatic evaluation methods, primarily based on similarity metrics, do not effectively capture the complex and independent attributes of counterspeech quality, such as contextual relevance, aggressiveness, or argumentative coherence. This has led to an increased dependency on labor-intensive human evaluations to assess automated counter-speech generation methods. To address these challenges, we introduce CSEval, a novel dataset and framework for evaluating counterspeech quality across four dimensions: contextual-relevance, aggressiveness, argument-coherence, and suitableness. Furthermore, we propose Auto-Calibrated COT for Counterspeech Evaluation (Auto-CSEval), a prompt-based method with auto-calibrated chain-of-thoughts (CoT) for scoring counterspeech using large language models. Our experiments show that Auto-CSEval outperforms traditional metrics like ROUGE, METEOR, and BertScore in correlating with human judgement, indicating a significant improvement in automated counterspeech evaluation.